A convolutional neural network which has recently shown performance similar to human cognitive ability in image processing is being actively researched. In general, the convolutional neural network uses a deep learning-based algorithm and is implemented with hardware in which a CPU, a GPU, a memory, and other operation logics are integrated.
The convolutional neural network hardware performs a convolution operation for producing an output value by applying a predetermined filter to image data in various stages. That is, the convolutional neural network hardware performs a dot-product operation between an input value and a filter weight value. This operation is relatively simply configured like multiply and add operations but has parameters with various and high values and thus is difficult to implement in hardware.
In order to process a huge amount of parameter, a large amount of data is loaded from a memory located outside a processor (or chip) into the processor, and this process consumes considerable power.
In this regard, Korean Laid-open Publication No. 10-2017-0023708 discloses a convolutional neural network computing apparatus.